Automate systematic review workflows from the search to analysis. Generate comprehensive reviews and meta-analyses with transparent methodology, data extraction, and statistical synthesis.
Rated by 1000+ Researchers and Universities
Traditional systematic reviews require searching multiple databases manually, taking 2-4 months for search phase.
Reviewing titles, abstracts, and full texts of 500-2000+ papers leads to inconsistent screening.
Manually extracting effect sizes, confidence intervals, and methodology details is tedious and error-prone.
Calculating pooled effect sizes, assessing heterogeneity, and conducting subgroup analyses requires extensive expertise.
Evaluating study quality using Cochrane Risk of Bias tools manually leads to inconsistent assessments.
Input your PICO question or systematic review objective. Paperguide's AI interprets your question and creates a structured search strategy with defined inclusion/exclusion criteria.
Example: What is the effectiveness of cognitive behavioral therapy compared to pharmacotherapy for treating major depressive disorder in adults?
Deep Research AI automatically searches across 200+ million papers using systematic methodology. It explores related concepts and MeSH terms for complete coverage.
AI screens papers against your specific inclusion criteria, evaluating study design, population, interventions, and outcomes. Papers are ranked and filtered with transparent reasoning.
Extract specific outcome measures, effect sizes, confidence intervals, and study characteristics across multiple studies in standardized tables for statistical analysis.
Know what researchers, students, doctors, and professionals are saying about Paperguide.
Evidence-Based Medicine Teamsconducting clinical systematic reviews for treatment guidelines and healthcare protocols
Academic Researchersperforming systematic reviews and meta-analyses for journal publications
Guideline Development Teamssynthesizing evidence for clinical practice guidelines and policy recommendations
Meta-analysis Specialistsextracting quantitative data across multiple studies for statistical synthesis
Healthcare Organizationsdeveloping evidence-based protocols and treatment recommendations
Replace months of manual searching with automated systematic review generation and comprehensive analysis.
Build standardized extraction tables with consistent outcome measures and effect sizes for meta-analysis.
Get immediate answers to specific review questions by analyzing multiple studies with synthesized citations.
Design extraction forms matching systematic review protocols with PICO elements and statistical data.
Make informed inclusion decisions based on journal quality and research impact with objective metrics.
Ask questions directly to individual studies to understand methodological differences and outcome definitions.